Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells40
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 KiB
Average record size in memory72.3 B

Variable types

Numeric7
Categorical2

Alerts

GRE Score is highly correlated with TOEFL Score and 6 other fieldsHigh correlation
TOEFL Score is highly correlated with GRE Score and 5 other fieldsHigh correlation
University Rating is highly correlated with GRE Score and 5 other fieldsHigh correlation
SOP is highly correlated with GRE Score and 5 other fieldsHigh correlation
LOR is highly correlated with GRE Score and 5 other fieldsHigh correlation
CGPA is highly correlated with GRE Score and 6 other fieldsHigh correlation
Research is highly correlated with GRE Score and 2 other fieldsHigh correlation
Chance of Admit is highly correlated with GRE Score and 6 other fieldsHigh correlation
GRE Score is highly correlated with TOEFL Score and 6 other fieldsHigh correlation
TOEFL Score is highly correlated with GRE Score and 5 other fieldsHigh correlation
University Rating is highly correlated with GRE Score and 5 other fieldsHigh correlation
SOP is highly correlated with GRE Score and 5 other fieldsHigh correlation
LOR is highly correlated with GRE Score and 5 other fieldsHigh correlation
CGPA is highly correlated with GRE Score and 6 other fieldsHigh correlation
Research is highly correlated with GRE Score and 2 other fieldsHigh correlation
Chance of Admit is highly correlated with GRE Score and 6 other fieldsHigh correlation
GRE Score is highly correlated with TOEFL Score and 3 other fieldsHigh correlation
TOEFL Score is highly correlated with GRE Score and 4 other fieldsHigh correlation
University Rating is highly correlated with GRE Score and 4 other fieldsHigh correlation
SOP is highly correlated with TOEFL Score and 4 other fieldsHigh correlation
LOR is highly correlated with SOPHigh correlation
CGPA is highly correlated with GRE Score and 4 other fieldsHigh correlation
Chance of Admit is highly correlated with GRE Score and 4 other fieldsHigh correlation
GRE Score is highly correlated with TOEFL Score and 4 other fieldsHigh correlation
TOEFL Score is highly correlated with GRE Score and 6 other fieldsHigh correlation
University Rating is highly correlated with GRE Score and 5 other fieldsHigh correlation
SOP is highly correlated with TOEFL Score and 4 other fieldsHigh correlation
LOR is highly correlated with TOEFL Score and 3 other fieldsHigh correlation
CGPA is highly correlated with GRE Score and 5 other fieldsHigh correlation
Research is highly correlated with GRE Score and 3 other fieldsHigh correlation
Chance of Admit is highly correlated with GRE Score and 6 other fieldsHigh correlation
GRE Score has 15 (3.0%) missing values Missing
TOEFL Score has 10 (2.0%) missing values Missing
University Rating has 15 (3.0%) missing values Missing
Serial No. is uniformly distributed Uniform
Serial No. has unique values Unique

Reproduction

Analysis started2022-08-14 18:40:10.033895
Analysis finished2022-08-14 18:40:28.987463
Duration18.95 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Serial No.
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:29.129222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.4818328
Coefficient of variation (CV)0.5767737835
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2022-08-15T00:10:29.328106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
3301
 
0.2%
3431
 
0.2%
3421
 
0.2%
3411
 
0.2%
3401
 
0.2%
3391
 
0.2%
3381
 
0.2%
3371
 
0.2%
3361
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
5001
0.2%
4991
0.2%
4981
0.2%
4971
0.2%
4961
0.2%
4951
0.2%
4941
0.2%
4931
0.2%
4921
0.2%
4911
0.2%

GRE Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct49
Distinct (%)10.1%
Missing15
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean316.5587629
Minimum290
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:29.678569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile298
Q1308
median317
Q3325
95-th percentile335
Maximum340
Range50
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.2747043
Coefficient of variation (CV)0.03561646565
Kurtosis-0.6844666911
Mean316.5587629
Median Absolute Deviation (MAD)8
Skewness-0.05168658259
Sum153531
Variance127.1189571
MonotonicityNot monotonic
2022-08-15T00:10:30.054559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
31222
 
4.4%
32422
 
4.4%
32217
 
3.4%
32117
 
3.4%
31617
 
3.4%
32717
 
3.4%
31416
 
3.2%
32016
 
3.2%
31116
 
3.2%
32515
 
3.0%
Other values (39)310
62.0%
ValueCountFrequency (%)
2902
 
0.4%
2931
 
0.2%
2942
 
0.4%
2955
1.0%
2965
1.0%
2976
1.2%
29810
2.0%
2998
1.6%
30012
2.4%
30110
2.0%
ValueCountFrequency (%)
3409
1.8%
3393
 
0.6%
3384
0.8%
3372
 
0.4%
3365
1.0%
3354
0.8%
3347
1.4%
3334
0.8%
3327
1.4%
3319
1.8%

TOEFL Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct29
Distinct (%)5.9%
Missing10
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean107.1877551
Minimum92
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:30.366883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98
Q1103
median107
Q3112
95-th percentile118
Maximum120
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.112899387
Coefficient of variation (CV)0.0570298294
Kurtosis-0.6645653663
Mean107.1877551
Median Absolute Deviation (MAD)5
Skewness0.1020677321
Sum52522
Variance37.36753892
MonotonicityNot monotonic
2022-08-15T00:10:30.650976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
11042
 
8.4%
10537
 
7.4%
10429
 
5.8%
10728
 
5.6%
11227
 
5.4%
10626
 
5.2%
10325
 
5.0%
10024
 
4.8%
10224
 
4.8%
9922
 
4.4%
Other values (19)206
41.2%
ValueCountFrequency (%)
921
 
0.2%
932
 
0.4%
942
 
0.4%
953
 
0.6%
966
 
1.2%
977
 
1.4%
9810
2.0%
9922
4.4%
10024
4.8%
10119
3.8%
ValueCountFrequency (%)
1209
 
1.8%
11910
 
2.0%
11810
 
2.0%
1178
 
1.6%
11616
3.2%
11511
2.2%
11418
3.6%
11318
3.6%
11227
5.4%
11120
4.0%

University Rating
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.0%
Missing15
Missing (%)3.0%
Memory size4.0 KiB
3.0
154 
2.0
124 
4.0
103 
5.0
72 
1.0
32 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1455
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0154
30.8%
2.0124
24.8%
4.0103
20.6%
5.072
14.4%
1.032
 
6.4%
(Missing)15
 
3.0%

Length

2022-08-15T00:10:30.961327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-15T00:10:31.282055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0154
31.8%
2.0124
25.6%
4.0103
21.2%
5.072
14.8%
1.032
 
6.6%

Most occurring characters

ValueCountFrequency (%)
.485
33.3%
0485
33.3%
3154
 
10.6%
2124
 
8.5%
4103
 
7.1%
572
 
4.9%
132
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number970
66.7%
Other Punctuation485
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0485
50.0%
3154
 
15.9%
2124
 
12.8%
4103
 
10.6%
572
 
7.4%
132
 
3.3%
Other Punctuation
ValueCountFrequency (%)
.485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.485
33.3%
0485
33.3%
3154
 
10.6%
2124
 
8.5%
4103
 
7.1%
572
 
4.9%
132
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.485
33.3%
0485
33.3%
3154
 
10.6%
2124
 
8.5%
4103
 
7.1%
572
 
4.9%
132
 
2.2%

SOP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.374
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:31.532133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.9910036208
Coefficient of variation (CV)0.2937177299
Kurtosis-0.7057169536
Mean3.374
Median Absolute Deviation (MAD)0.5
Skewness-0.2289723963
Sum1687
Variance0.9820881764
MonotonicityNot monotonic
2022-08-15T00:10:31.799007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
489
17.8%
3.588
17.6%
380
16.0%
2.564
12.8%
4.563
12.6%
243
8.6%
542
8.4%
1.525
 
5.0%
16
 
1.2%
ValueCountFrequency (%)
16
 
1.2%
1.525
 
5.0%
243
8.6%
2.564
12.8%
380
16.0%
3.588
17.6%
489
17.8%
4.563
12.6%
542
8.4%
ValueCountFrequency (%)
542
8.4%
4.563
12.6%
489
17.8%
3.588
17.6%
380
16.0%
2.564
12.8%
243
8.6%
1.525
 
5.0%
16
 
1.2%

LOR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.484
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:32.057723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9254495739
Coefficient of variation (CV)0.2656284655
Kurtosis-0.7457485106
Mean3.484
Median Absolute Deviation (MAD)0.5
Skewness-0.1452903146
Sum1742
Variance0.8564569138
MonotonicityNot monotonic
2022-08-15T00:10:32.306300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
399
19.8%
494
18.8%
3.586
17.2%
4.563
12.6%
2.550
10.0%
550
10.0%
246
9.2%
1.511
 
2.2%
11
 
0.2%
ValueCountFrequency (%)
11
 
0.2%
1.511
 
2.2%
246
9.2%
2.550
10.0%
399
19.8%
3.586
17.2%
494
18.8%
4.563
12.6%
550
10.0%
ValueCountFrequency (%)
550
10.0%
4.563
12.6%
494
18.8%
3.586
17.2%
399
19.8%
2.550
10.0%
246
9.2%
1.511
 
2.2%
11
 
0.2%

CGPA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct184
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.57644
Minimum6.8
Maximum9.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:32.640648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile7.638
Q18.1275
median8.56
Q39.04
95-th percentile9.6
Maximum9.92
Range3.12
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.6048128003
Coefficient of variation (CV)0.07052026253
Kurtosis-0.5612783981
Mean8.57644
Median Absolute Deviation (MAD)0.46
Skewness-0.02661251732
Sum4288.22
Variance0.3657985234
MonotonicityNot monotonic
2022-08-15T00:10:32.989607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.769
 
1.8%
89
 
1.8%
8.127
 
1.4%
8.457
 
1.4%
8.547
 
1.4%
8.567
 
1.4%
8.656
 
1.2%
7.886
 
1.2%
9.116
 
1.2%
9.046
 
1.2%
Other values (174)430
86.0%
ValueCountFrequency (%)
6.81
0.2%
7.21
0.2%
7.211
0.2%
7.231
0.2%
7.251
0.2%
7.281
0.2%
7.31
0.2%
7.342
0.4%
7.361
0.2%
7.41
0.2%
ValueCountFrequency (%)
9.921
 
0.2%
9.911
 
0.2%
9.872
0.4%
9.861
 
0.2%
9.821
 
0.2%
9.83
0.6%
9.781
 
0.2%
9.762
0.4%
9.741
 
0.2%
9.72
0.4%

Research
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
280 
0
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1280
56.0%
0220
44.0%

Length

2022-08-15T00:10:33.314166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-15T00:10:33.563779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1280
56.0%
0220
44.0%

Most occurring characters

ValueCountFrequency (%)
1280
56.0%
0220
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1280
56.0%
0220
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1280
56.0%
0220
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1280
56.0%
0220
44.0%

Chance of Admit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72174
Minimum0.34
Maximum0.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-08-15T00:10:33.854863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.47
Q10.63
median0.72
Q30.82
95-th percentile0.94
Maximum0.97
Range0.63
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.141140404
Coefficient of variation (CV)0.1955557458
Kurtosis-0.4546817998
Mean0.72174
Median Absolute Deviation (MAD)0.1
Skewness-0.28996621
Sum360.87
Variance0.01992061363
MonotonicityNot monotonic
2022-08-15T00:10:34.471572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7123
 
4.6%
0.6419
 
3.8%
0.7318
 
3.6%
0.7216
 
3.2%
0.7916
 
3.2%
0.7815
 
3.0%
0.7614
 
2.8%
0.6213
 
2.6%
0.9413
 
2.6%
0.713
 
2.6%
Other values (51)340
68.0%
ValueCountFrequency (%)
0.342
 
0.4%
0.362
 
0.4%
0.371
 
0.2%
0.382
 
0.4%
0.391
 
0.2%
0.424
0.8%
0.431
 
0.2%
0.443
0.6%
0.453
0.6%
0.465
1.0%
ValueCountFrequency (%)
0.974
 
0.8%
0.968
1.6%
0.955
 
1.0%
0.9413
2.6%
0.9312
2.4%
0.929
1.8%
0.9110
2.0%
0.99
1.8%
0.8911
2.2%
0.884
 
0.8%

Interactions

2022-08-15T00:10:25.461712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:11.366961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:13.692167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:15.948072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:18.080641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:20.668967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:23.140891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:25.744038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:11.584342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:14.024954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:16.250559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:18.479141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:21.103280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:23.441677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:26.041541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:11.778361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:14.307823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:16.532567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:18.785766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:21.484052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:23.935957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:26.332304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:12.078863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:14.597807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:16.841241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:19.081335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:21.783041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:24.229476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:26.654243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:12.755660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:14.921863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:17.176933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:19.432937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:22.123756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:24.548039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:27.018737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:12.956315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:15.289522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:17.484670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:19.789030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:22.480521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:24.865491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:27.318074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:13.311450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:15.648493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:17.751403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:20.236525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:22.799209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-15T00:10:25.162817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-08-15T00:10:34.806335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-15T00:10:35.255724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-15T00:10:35.669270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-15T00:10:35.972675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-15T00:10:36.198795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-15T00:10:27.921997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-15T00:10:28.407042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-15T00:10:28.709308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-08-15T00:10:28.845290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
01337.0118.04.04.54.59.6510.92
12324.0107.04.04.04.58.8710.76
23NaN104.03.03.03.58.0010.72
34322.0110.03.03.52.58.6710.80
45314.0103.02.02.03.08.2100.65
56330.0115.05.04.53.09.3410.90
67321.0109.0NaN3.04.08.2010.75
78308.0101.02.03.04.07.9000.68
89302.0102.01.02.01.58.0000.50
910323.0108.03.03.53.08.6000.45

Last rows

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
490491307.0105.02.02.54.58.1210.67
491492297.099.04.03.03.57.8100.54
492493298.0101.04.02.54.57.6910.53
493494300.095.02.03.01.58.2210.62
494495301.099.03.02.52.08.4510.68
495496332.0108.05.04.54.09.0210.87
496497337.0117.05.05.05.09.8710.96
497498330.0120.05.04.55.09.5610.93
498499312.0103.04.04.05.08.4300.73
499500327.0113.04.04.54.59.0400.84